Computing apparatus of the neural network type has been described, for instance, by J. J. Hopfield in "Neurons With Graded Response Have Collective Computational Properties Like Those of Two-state Neurons"; Proc. Natl. Acad. Sci., USA Vol. 81, pp. 3088-3092 (1984), and by J. J. Hopfield and D. W. Tank in "Neural Computation of Decisions in Optimization Problems", Biological Cybernetics, Vol. 52, (1985), pp. 141-152; as well as in U.S. patent application Ser. No. 693,479 filed on behalf of J. J. Hopfield on Jan. 2, 1985, U.S. Pat. No. 4,660,166 and U.S. patent application Ser. No. 795,789 filed on behalf of J. J. Hopfield and D. W. Tank on Nov. 7, 1985, U.S. Pat. No. 4,719,591.
Basically, a so-called neural network is a highly parallel computational circuit comprising a plurality of (typically nonlinear) amplifiers, with typically each of the amplifiers feeding back its output signal to all of the other amplifiers through resistors of resistances R.sub.ij. The resistors (R.sub.ij is the resistance of the resistor between the output of amplifier j and the input of amplifier i) and the associated connections can be thought of as comprising a feedback network which has one output signal set and two input signal sets. The output signal set is applied to the amplifier inputs, one of the input signal sets is derived from the amplifier outputs, and the other input signal set is responsive to input stimuli applied to the neural network. As shown in the prior art, one can explicitly specify the values of R.sub.ij to achieve predetermined results, such as reaching different specified output states of the amplifiers in response to different ranges of input stimuli.
According to the prior art, the specific problem to be solved is programmed, inter alia, by selecting appropriate values of the resistances R.sub.ij. Since neural networks or other collective decision networks can be expected to consist of dozens, more likely hundreds or even thousands of amplifiers, such neural networks will contain tens of thousands or even millions of resistors. Clearly, such circuits can only be implemented by means of large scale integration.
The prior art knows neural networks implemented on a semiconductor chip, ref. and such networks have been used to solve such computational problems as the traveling salesman problem. However, these prior art networks are quite restricted in their applicability, since no practical way is known to the prior art for reprogramming such a chip, i.e., changing the values of the resistors. A different computer chip is thus necessary in order to solve a different problem. This clearly is not acceptable in practice, and a technique that allows reprogramming of the resistors is urgently needed, to be able to solve a variety of problems using the same chip. This application discloses an electrically programmable resistor which can advantageously be used in computing apparatus, including neural networks and the like.
The prior art knows that the resistance of samples of some materials can be changed by application of an appropriate electric field across such a sample. However, in these prior art materials the resistance change typically is due to an irreversible structural change in the material. See for instance, M. Tanimoto, et al, IEEE Transactions on Electron Devices, Vol. ED-27(3) pp. 517-520 (1980). Due to the irreversible nature of the change in these prior art materials, they do not lend themselves to the formation of programmable resistors of the type needed in computing apparatus as described above since, inter alia, once a resistor is formed in the material the material cannot conveniently be "reset" to its original high resistance state, or to some other high resistance state.